In 5G mobile communication standard, polar codes have been used as coding on control channels. Although the polar code has good performance, the performance of its short code is still facing huge challenges. Moreover, the size limitation of using the deep learning model as the decoder of the polar code has always been the focus of researchers seeking a breakthrough. In this paper, the convolutional neural network (CNN) model in deep learning is cascaded with a successive cancellation (SC) decoder to achieve the decoding of polar codes under additive correlated Gaussian noise (ACGN) channels, thereby improving the decoding performance of short polar codes under ACGN and alleviating the code length limitation of a single deep learning model as a decoder. The cascaded decoding structure is represented as a CNN-SC decoder, which can effectively estimate colored noise. The simulation shows that the CNN-SC decoder improves the performance of polar codes under ACGN channels significantly.